Bi-regional dynamic contrast-enhanced MRI for prediction of microvascular invasion in solitary BCLC stage A hepatocellular carcinoma

Objectives To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy. Methods Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (CDCE) were constructed to predict MVI. The combined model incorporating CDCE and CR features was developed and evaluated. Kaplan–Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches. Results A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. CDCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and CDCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p < 0.05). Conclusion The combined model integrating DCE perfusion parameters and CR features could be used for MVI prediction in HCC patients and assist clinical decision-making. Critical relevance statement The combined model incorporating bi-regional DCE-MRI perfusion parameters and CR features predicted MVI preoperatively, which could stratify the risk of recurrence and aid in optimizing treatment strategies. Key Points Microvascular invasion (MVI) is a significant predictor of prognosis for hepatocellular carcinoma (HCC). Quantitative DCE-MRI could predict MVI in solitary BCLC stage A HCC; the combined model improved performance. The combined model could help stratify the risk of recurrence and aid treatment planning. Graphical Abstract


Appendix E2 Perfusion DCE-MR image processing
The multi-phase DCE MR images were registered by GenIQ software package on Advantage Workstation version 4.7 (GE Healthcare, USA) for pre-processing prior to further analysis.The registered DCE-MR images were processed and analyzed using an inhouse program written on MATLAB R2018a (MathWorks, Natick, MA, USA).First, two round region of interest (ROI) was manually placed on abdominal aorta and main portal vein at level of porta hepatis for arterial input function (AIF) and portal vein input function (VIF) calculation, which were used as an estimation for the hepatic arterial and portal venous blood supply, respectively.Then, a third ROI was manually traced along the edge of the liver at the slice of maximum tumor diameter.The signal intensity on MRI was converted into an equivalent concentration of contrast material using multiple flip angles method and T1 fitting.Subsequently, the contrast concentration curves of the ROIs were calculated using a dual-input single compartment model, which depicts contrast material distribution after injection and predicts a change in the contrast concentration in the tissue as a function of time, C(t), using the below equation according to previously described [1,2]: where C(t), Ca(t), and Cp(t) were the concentrations of contrast material in the tissue, aorta, and portal vein, and, respectively; k1a, k1p, and k2 were constants for the aortic inflow rate, the portal venous inflow rate, and the outflow rate, respectively.

MR imaging feature Definitions
LI-RADS major features [1] Tumor size, cm Largest outer-edge-to-outer-edge dimension of an observation.
No-rim arterial phase hyperenhancement Nonrim-like enhancement in arterial phase unequivocally greater in whole or in part than liver.

Non-peripheral washout
Nonperipheral visually assessed temporal reduction in enhancement in whole or in part relative to composite liver tissue from earlier to later phase resulting in hypoenhancement in the extracellular phase.
Enhancing capsule Smooth, uniform, sharp border around most or all of an observation, unequivocally thicker or more conspicuous than fibrotic tissue around background nodules, and visible as enhancing rim in PVP, DP, or TP.

LI-RADS ancillary features (favoring HCC in particular)
Non-enhancing capsule Capsule appearance not visible as an enhancing rim.
Tumor margin Defined on the portal venous phase and/or delayed phase, and was categorized as i) smooth margin presenting as nodular tumors with smooth contour, and ii) non-smooth margin presenting as an irregular margin that had budding portion at the tumor periphery protruding into the liver parenchyma [2].
Tumor capsule A thin, linear and hyperenhancement structure surrounded the tumor border in the portal venous and/or delayed phase and grouped into absent, incomplete, and intact types [3].

TTPVI
The presence of internal arteries visible in the arterial phase and the absence of hypointense halo in a post-arterial phase [4].

Fig. S1 .
Fig. S1.Box and whisker plots of the show the distribution of each perfusion DCE parameter of intra-tumoral region in the training set between MVI positive and MVI negative groups.Box and whisker plots of ART (a), Fa (b), Fp (c), Ft (d), DV (e) and MTT (f) between MVI positive and MVI negative groups, respectively.Boxes show the upper and lower quartiles, and horizontal lines within boxes indicate median values, whiskers indicate first quartile minus 1.5 times inner quartile range and third quartile plus 1.5 times inner quartile range, and circles indicate outliers.Asterisks indicate significant differences between MVI positive and MVI negative groups (P < 0.05).NS indicate no significant differences between MVI positive and MVI negative groups (P > 0.05).MVI, microvascular invasion.

Fig. S2 .
Fig. S2.Box and whisker plots of the show the distribution of each perfusion DCE parameter of peri-tumoral region in the training set between MVI positive and MVI negative groups.Box and whisker plots of ART (a), Fa (b), Fp (c), Ft (d), DV (e) and MTT (f) between MVI positive and MVI negative groups, respectively.Boxes show the upper and lower quartiles, and horizontal lines within boxes indicate median values, whiskers indicate first quartile minus 1.5 times inner quartile range and third quartile plus 1.5 times inner quartile range, and circles indicate outliers.Asterisks indicate significant differences between MVI positive and MVI negative groups (P < 0.05).NS indicate no significant differences between MVI positive and MVI negative groups (P > 0.05).MVI, microvascular invasion.

Fig. S3 .
Fig. S3.Box and whisker plots of the show the distribution of each perfusion DCE parameter of intra-tumoral region in the validation set between MVI positive and MVI negative groups.Box and whisker plots of ART (a), Fa (b), Fp (c), Ft (d), DV (e) and MTT (f) between MVI positive and MVI negative groups, respectively.Boxes show the upper and lower quartiles, and horizontal lines within boxes indicate median values, whiskers indicate first quartile minus 1.5 times inner quartile range and third quartile plus 1.5 times inner quartile range, and circles indicate outliers.Asterisks indicate significant differences between MVI positive and MVI negative groups (P < 0.05).NS indicate no significant differences between MVI positive and MVI negative groups (P > 0.05).MVI, microvascular invasion.

Fig. S4 .
Fig. S4.Box and whisker plots of the show the distribution of each perfusion DCE parameter of peri-tumoral region in the validation set between MVI positive and MVI negative groups.Box and whisker plots of ART (a), Fa (b), Fp (c), Ft (d), DV (e) and MTT (f) between MVI positive and MVI negative groups, respectively.Boxes show the upper and lower quartiles, and horizontal lines within boxes indicate median values, whiskers indicate first quartile minus 1.5 times inner quartile range and third quartile plus 1.5 times inner quartile range, and circles indicate outliers.Asterisks indicate significant differences between MVI positive and MVI negative groups (P < 0.05).NS indicate no significant differences between MVI positive and MVI negative groups (P > 0.05).MVI, microvascular invasion.

Fig. S5 .
Fig. S5.Heat maps show the p values of the Delong test used for comparing the areas under the curves of different predicting models for microvascular invasion in hepatocellular carcinoma.CR, clinic-radiological; DCE, dynamiccontrast enhanced; ITR, intra-tumoral region; PTR, peritumoral region

Table S1 .
MR imaging acquisition protocol and main sequence parameters

Table S2 .
Definition of each MR imaging feature in this study

Table S5 .
Construction of clinic-radiological, DCE, and combined models through multivariate logistic analysis A stepwise forward method was used to assess the best independent predictor of microvascular invasion AFP, alpha fetoprotein; ART, arterial fraction; β, coefficient; CDCE, combined quantitative parameter of DCE-MRI; CI, confidence interval; DCE, dynamic-contrast enhanced; Ft, total blood flow; MTT, mean transit time; OR, odds ratio; P, peritumoral region; S.E., standard error; T, intra-tumoral region; TTPVI, two-trait predictor of venous invasion Insights Imaging (2024) Zhu Y, Feng B, Wang P, et al.*